Causal discovery, i. e., inferring underlying cause-effect relationships from observations of a scene or system, is an inherent mechanism in human cognition, but has been shown to be highly challenging to automate.
Inspired by human learning, we illustrate that scheduling over which tasks to revisit is critical to the final performance with finite memory resources.
no code implementations • 5 Sep 2017 • Patrik Jonell, Joseph Mendelson, Thomas Storskog, Goran Hagman, Per Ostberg, Iolanda Leite, Taras Kucherenko, Olga Mikheeva, Ulrika Akenine, Vesna Jelic, Alina Solomon, Jonas Beskow, Joakim Gustafson, Miia Kivipelto, Hedvig Kjellstrom
This paper presents the EACare project, an ambitious multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e. g., due to Alzheimer's disease.
The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data.
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings.
The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.
The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data.
In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data.